Introduction

In this tab, we take a look at the the tweets advocating for greater access to water around the world in order to discover some interesting trends among those tweets. Specifically, in order to gather up the tweets, search_tweets() function was run on May 4th and May 7th, and the tweets generated roughly from April 27th to May 7th were recorded in the same dataset. The included tweets all include at least one of the following phrases: “water access,” or “Water access,” or “Water Access,” or “access to clean water,” or “access to drinking water.” For more details, take a look at the “Wrangling - Masahiro” file in the same repo. Through exploring the following three questions with data, we aim to learn about what kind of rhetoric people are employing in an attempt to claim for more access to water around the world.

• What are the common words used in the tweets requesting more access to clean water around the world?
• What are the common sentiments of the words observed in those tweets?
• What do those common words and sentiments imply about people’s rhetorics arguing for clean water in some of the regions lacking water access?

In addition to the removal of so-called stop words from the dataset, we also omitted the word “access” because it is obvious that all the tweets should include the word “access” from the way we collected data. Doing so helps us produce more meaningful word clouds and sentiment analyses.

Word Cloud

First, we examine the word cloud addressing all the words except the ones displaced through data wrangling in order to get a sense about what are some of the most common words utilized in the focal tweets.

In the above word cloud, “https” stands out in its size, which implies that a lot of tweets related to water access advocacy refer to or cite other web resources. Also, “clean” is displayed largely in the visualization, which should be partly because “access to clean water” is one of the phrases we actively searched for when scraping tweets. However, given that we also looked for “access to drinking water” when gathering text while the word “drinking” does not have equally big size in the display as “clean,” it seems like that the word “clean” possesses a particularly great importance for arguments for greater water access across the earth. Paying attention to other words displayed with smaller sizes, it can be seen that the cloud includes a lot of words related to potential use of water or implication of access to water: “sanitation,” “healthcare,” “health,” “food,” and “hygiene.” Also, one of the interesting words to be observed in the cloud is “india,” whose presence may be attributable to the socioeconomic standing of India as a country or the nation’s especially large population.

Sentiment Analysis

Next, we dive into the sentiments reflected in the usage of English by those advocating for water access on twitter. We use the NRC lexicon for attaching sentimental implications for words observed in tweets, and visualize the common sentiment in the tweets with the following graph.

As can be seen, positive, trust, and joy are the most popular sentiments among the words included in the tweets. Negative follows those top three sentiments, and then, the least popular sentiments such as anger, anticipation, fear, and sadness occupy the subsequent places. With this bar chart, we verify that a lot of words employed in the analyzed tweets have some positive connotations, which not only refers to “positive” as a sentiment but also “trust” and “joy.” In order to learn more about the use of words detected as implying these sentiments, we have decided to utilize the comparison cloud (see the next tab).

Comparison Cloud

The below comparison cloud displays what words are commonly used in the text scraped from twitter while also having implications of “positive,” “trust,” or “joy.” Before diving into the detailed observations about the visualization itself, we lay out how the code below works. A comparison cloud enables users to accomplish two goals simultaneously: comparing the relative frequency of the use of certain words and classifying the most commonly used words into several categories based upon certain criteria. In order to craft a comparison cloud, however, it is necessary to transform the data into the form of matrix, whose column corresponds to certain categories (in this case, the sentiment) and whose row refers to each word by its name. In order to craft such a matrix, a lot of wrangling has been conducted to create a dataset whose row corresponds to words and column to each sentiment. If interested, analyze the commented code below.

# preliminary wranglings below
# first extract words with the connotations of interest
# tweet_sentiment = dataset used for sentiment analysis
pure_words <- tweets_sentiment %>%
  filter(sentiment == "positive" | sentiment == "trust" |
           sentiment == "joy") %>%
  # then collapse the rows so that each word only occupies a single row
  group_by(word) %>%
  summarize()
# now prepare the dataset to be joined with the dataset about the count of
# each word with the three focal sentiments
pure_words_copied <- pure_words %>%
  # let each word occupy three rows at the same time
  slice(rep(1:n(), each = 3)) %>%
  mutate(number = row_number()) %>%
  # list up all the sentiments of interest
  mutate(sentiment = case_when(number %% 3 == 1 ~ "positive",
                               number %% 3 == 2 ~ "trust",
                               number %% 3 == 0 ~ "joy")) %>%
  select(word, sentiment)
# the below dataset is about the count of each word with the three connotations
# of interest
comparison_words_prep <- tweets_sentiment %>%
  # extract those with the three sentiments of innterest
  filter(sentiment == "positive" | sentiment == "trust" |
           sentiment == "joy") %>%
  # and count the frequency
  group_by(word, sentiment) %>%
  summarize(N = n())
comparison_words_prep_2 <- pure_words_copied %>%
  # join the dataset with the data about the count (used for the bar)
  left_join(comparison_words_prep, by = c("word", "sentiment")) %>%
  # if some words do not imply certain sentiments, it will be reflected as 
  # N/A values, so turn it into 0
  mutate(count = case_when(is.na(N) ~ 0,
                           TRUE ~ as.numeric(N))) %>%
  select(word, sentiment, count)
# one last step to make each column refer to each sentiment
comparison_words_prep_3 <- comparison_words_prep_2 %>%
  spread(key = sentiment, value = count)
# the below code translates the data frame into a matrix, and each row name of
# the matrix should correspond to the word
comparison_words <- comparison_words_prep_3 %>%
  select(-word) %>%
  as.matrix()
rownames(comparison_words) <- comparison_words_prep_3$word

# create the comparison cloud
colors1 <- c("#48F11F", "#1226D2", "#CB0A3E")
colors2 <- c("#CCFF99", "#7F88EF", "#EF7FCA")
comparison.cloud(comparison_words, max.words = 100,
                 random.order = FALSE,
                 colors = colors1,
                 title.colors = colors1,
                 title.bg.colors = colors2)

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